Global Matching Feature of Reference View.    Besides the local, pixel-wise features extracted from CNNs, we also leverage global, long-range information to better guide the matching. Towards that, a transformer layer (four-head self-attention with positional encoding) [1]} is applied to the local feature \(f_0\)  of the reference view, to construct an aggregated feature \(f_0^a \in \mathbb {R}^ {H/4 \times W/4 \times F_1}\)  as
\(f_0^a = f_0 + \omega _\alpha \cdot \text{softmax} \left(\frac{(f_0 \cdot W^Q) \cdot (f_0 \cdot W^K)^T}{\sqrt{F_1}} (f_0 \cdot W^V )\right)\) 
